Tag: Raspberry Pi

UPDATE:

In this presentation, we will look at the how users interface with machines without the use of touch. These different types of interaction have their benefits and pitfalls. To showcase the power of these user interactions we will explore: Voice commands with mobile applications, Speech Recognition, and Computer Vision. After this presentation, attendees will have the knowledge to create applications that can utilize voice, video, and machine learning.

Users use voice (Alexa, Cortana, Google Now) or video as a mode of interaction with applications. More than a fad, this is a natural interface for users and is becoming more and more common with the ever-decreasing size of hardware.

Different types of interaction have their benefits and pitfalls. To showcase the power of these user interactions we will explore: Voice commands with two app types: UWP and Xamarin Forms (iOS and Android). Speech Recognition with Cognitive Services: Verifying the speaker with Speaker Recognition API. Computer Vision with Cognitive Services: Verifying a user with Face API.

By utilizing UWP, Xamarin, and Cognitive services; a device with the ultimate in customization for user interactions will be created. Come and see how!

Original:

Being able to run compute cycles on local hardware is a practice predating silicon circuits. Mobile and Web technology has pushed computation away from local hardware and onto remote servers. As prices in the cloud have decreased, more and more of the remote servers have moved there. This technology cycle is coming full circle with pushing the computation that would be done in the cloud down to the client. The catalyst for the cycle completing is latency and cost. Running computations on local hardware softens the load in the cloud and reduces overall cost and architectural complexity.

The difference now is how the computational logic is sent to the device. As of now, we rely on app stores and browsers to deliver the logic the client will use. Delivery mechanisms are evolving into writing code once and having the ability to run that logic in the cloud and push that logic to the client through your application and have that logic run on the device. In this presentation, we will look at how to accomplish this with existing Azure technologies and how to prepare for upcoming technologies to run these workloads.

If running Edge on a Raspberry Pi and an Edge container’s logs show ‘exec user process caused “exec format error”‘ as an error then most likely you are running a non Raspberry Pi container on the Raspberry Pi. If the docker file used to build the container starts with:

Publish to the Raspberry Pi

On macOS and Linux, open a terminal window. On Windows, open a command prompt and run:

dotnet clean .
dotnet restore .
dotnet build .

This will rebuild the solution. Once that is complete run the following command:

dotnet publish . -r linux-arm

This will generate all the files needed for running the solution on the Raspberry Pi. After this command completes generating the needed files, the files need to be deployed to the Pi. Run the following command in Powershell from the publish folder:

Install .NET Core to the Raspberry Pi

The following commands need to be run on the Raspberry Pi whilst connected over an SSH session or via a terminal in the PIXEL desktop environment.

Run sudo apt-get install curl libunwind8 gettext. This will use the apt-get package manager to install three prerequiste packages.

Run curl -sSL -o dotnet.tar.gz https://dotnetcli.blob.core.windows.net/dotnet/Runtime/release/2.0.0/dotnet-runtime-latest-linux-arm.tar.gz to download the latest .NET Core Runtime for ARM32. This is refereed to as armhf on the Daily Builds page.

Run sudo ln -s /opt/dotnet/dotnet /usr/local/bin` to set up a symbolic link…a shortcut to you Windows folks 😉 to the dotnet executable.

Test the installation by typing dotnet –help.

Try to create a new .NET Core project by typing dotnet new console. Note this will prompt you to install the .NET Core SDK however this link won’t work for Raspian on ARM32. This is expected behaviour.

Run your application on the Raspberry Pi

Finally to run your application on the Raspberry Pi, navigate to the folder where the application was published and run the following command:

dotnet run

You can then navigate to the website hosted by the Raspberry Pi by navigating to http://{your IP address here}:5000.